# Applied AI, built for real use cases Reliable enterprise-grade solutions tailored to data, processes, and compliance with human oversight [Tell us your use case](/contact) ![](/sites/default/files/clone-images/666159944ebe725063986847_img_11_275cd46a.webp) ![](/sites/default/files/clone-images/667568c115626502b8f1e16f_img_13bfd037.webp) ![](/sites/default/files/clone-images/6675689a7db42ebf172efb4f_img-1_6c229be9.webp) Addressing business opportunities with AI-powered solutions, alongside a team of domain experts ## Services ![Browser](/sites/default/files/clone-images/649ec06a729f8eda3d3c6065_browser_2a6c42f8.svg)### Retrieval-Augmented Generation Retrieval-Augmented Generation (RAG) improves the reliability of AI by grounding responses in trusted data sources. Rather than relying only on pre-trained models, it retrieves information from documents, databases, or APIs before generating answers, making outputs accurate, contextual, and traceable. **We help teams apply RAG securely, with governance and data pipelines for confident adoption.** ![AI Driven Personalisation](/sites/default/files/clone-images/6585360caf88290ed404e4e4_AI_Driven_Personalisation_8335b945.svg)### Agent-Based and Multi-Agent Workflows Agent-based systems carry out structured, repeatable tasks that slow teams down when handled manually. They can fetch data, follow rules, and pass information between systems. When several agents work together, they form multi-agent systems. With the Model Context Protocol (MCP), tasks can be divided, verified, and reassigned if one part fails. **We design and implement these systems so routine work is handled automatically, and humans only step in where their judgment adds value.** ![AI assisted CMS workflows](/sites/default/files/clone-images/658535f9851ea6c9590a8abd_AI_assisted_CMS_workflows_ad1c58d8.svg)### Voice and text assistants AI assistants handle domain-specific queries across chat, voice, and product interfaces. They can run tasks in connected systems, answer routine questions, and hand requests to people when needed. Use cases range from internal helpdesks and IVR call flows to product guidance and support. **We create assistants that adapt to different channels and interaction styles, so users get consistent help whether they type, tap, or speak.** ![Icon wrapper](/sites/default/files/clone-images/6788f8b1f779eb0b670a5f06_icon-wrapper_2_add2d2fe.svg)### AI Consulting ​​AI consulting helps organizations move past pilots and see results. It starts with mapping workflows and running pilots that test if automation cuts manual work, speeds up reporting, or reduces errors. When pilots succeed, the next step is to make them part of daily operations with the right checks and metrics. **We help teams double down on what works and stop spending time and money on what doesn’t.** ![AI ML Consulting](/sites/default/files/clone-images/65292ea929dc3d51f845a34c_AI_ML_Consulting_be6e522e.svg)### AI Monitoring and Evaluation AI monitoring keeps deployed models reliable by tracking drift, bias, and performance drops that traditional monitoring often misses. Each system logs usage and outputs, with review loops so teams can audit results and enforce governance. This oversight is critical where accuracy and compliance cannot be compromised. **We help teams set up monitoring that makes AI dependable in practice.** ![Chatbot Conversational AI](/sites/default/files/clone-images/65853637851ea6c9590abb20_Chatbot_Conversational_AI_6f9a7fd7.svg)### Fine tuning Foundational/Small Models We tailor foundational and small language models to your domain for sharper accuracy and context awareness. Using efficient fine-tuning methods like LoRA and PEFT. **We deliver high-performing, cost-effective models that align with your business needs and are ready for rapid deployment.** ![Illinois Legal Aid Online AI-powered semantic search and conversational assistant by QED42](/sites/default/files/clone-images/ilao_image_1.avif) ![ILAO](/sites/default/files/clone-images/650480ecb779a776deb4ea62_height_96_brand_ILAO_2d10dba0.svg)## Building AI systems to improve public legal help for ILAO Over nine years, QED42 partnered with Illinois Legal Aid Online. The latest phase introduced semantic search and a conversational assistant powered by RAG, grounding answers in verified legal content. 9+ Years of engagement 5000+ Monthly search sessions 2x Faster path to content [Read Case Study](/work/building-domain-aware-ai-systems-to-improve-public-legal-help-for-ilao) ## Validate AI impact with a 2-week, risk-free pilot tailored to your use case [ Tell us your use case ](/contact) ## Platforms We work across cloud platforms, selecting the right AI services for each use case rather than defaulting to one provider. ![](/sites/default/files/clone-images/aws_logo.avif) ### Build with AWS AI Amazon Bedrock AgentCore Textract Rekognition SageMaker [Explore AWS AI →](/ai-solutions/build-with-aws-ai) ![](/sites/default/files/clone-images/69c513501fa6eb207c0a18bc_ChatGPT_Image_Mar_26_2026_04_40_24_PM.avif) ### Build with Azure AI Azure OpenAI Service Copilot Studio AI Search Document Intelligence [Explore Azure AI →](/ai-solutions/build-with-azure-ai) ## Our AI Values ![Ethical Responsibility](/sites/default/files/clone-images/667260deb4b2ac9de7b927bf_Ethical_Responsibility_1_864abfa2.svg)### AI for good AI should be used to solve real-world problems, promote fairness, and improve lives. From addressing accessibility challenges to reducing inequalities, AI can create a positive social impact when implemented responsibly ![Continuous Learning Improvement](/sites/default/files/clone-images/667260deb2cae72d4892d78a_Continuous_Learning_Improvement_2_31c2846f.svg)### Human-in-loop AI works best when combined with human oversight. Including people in critical steps ensures accuracy, improves decision-making, and keeps the system aligned with real-world needs ![Quality Reliability](/sites/default/files/clone-images/667260deed6126ebf1029131_Quality_Reliability_1_2126625e.svg)### Adapting to context AI should continuously learn and adjust based on new data, changing user behaviours, and evolving business needs. This ensures it remains relevant, effective, and aligned with the context in which it operates ![Customer Centric Approach](/sites/default/files/clone-images/667260de22b68b032a993f8d_Customer-Centric_Approach_1_62040c86.svg)### Ethics first Ethical AI means being fair, transparent, secure and private. It involves spotting and reducing biases in systems and making decisions that users can trust. Accountability at every stage builds long-term reliability in AI solutions {"@context":"https://schema.org","@type":"FAQPage","@id":"https://qed42.com/services/ai-services#faq","isPartOf":{"@id":"https://qed42.com/services/ai-services#webpage"},"mainEntity":[{"@type":"Question","name":"We ran a pilot that worked. Why is production so different?","acceptedAnswer":{"@type":"Answer","text":"Pilots run on clean data, limited users, and relaxed constraints. Production means integrating with legacy systems, handling edge cases, enforcing access controls, and monitoring for drift over time. Most AI projects stall here because the engineering and governance work was not scoped into the pilot. We structure pilots with production requirements built in from day one so the transition is planned, not improvised."}},{"@type":"Question","name":"How do you handle data sovereignty and compliance when building AI systems?","acceptedAnswer":{"@type":"Answer","text":"Every AI engagement starts with understanding where your data lives, which regulations apply, and what your internal policies require. We architect AI systems so that data stays within your approved infrastructure, whether that is a specific cloud region, an on-premises environment, or a hybrid setup. Model training, inference, and storage are scoped to your jurisdictional and compliance requirements, including GDPR, HIPAA, and regional data residency laws. We do not default to a single hosting model. The architecture is shaped by your data governance needs, not ours."}},{"@type":"Question","name":"What does AI monitoring actually involve after deployment?","acceptedAnswer":{"@type":"Answer","text":"After deployment, models can drift as data changes, user behaviour shifts, or source content is updated. Monitoring means tracking output quality, flagging accuracy drops, logging usage patterns, and running periodic evaluations against ground truth. It also includes governance reviews so teams can audit what the system produced and why. Without this, deployed AI degrades silently."}},{"@type":"Question","name":"Can AI integrate with our existing Drupal or CMS platform?","acceptedAnswer":{"@type":"Answer","text":"Yes. Most of our AI work connects to existing content platforms, not replaces them. RAG systems pull from your CMS content. AI assistants surface answers from documents already in your system. Agents trigger actions in tools your teams use. If you are on Drupal, we have 17 years of platform expertise and lead the Drupal AI initiative, so the integration is tighter than what a standalone AI vendor would deliver."}},{"@type":"Question","name":"How long before we see measurable results from AI?","acceptedAnswer":{"@type":"Answer","text":"A scoped AI pilot typically runs two weeks and produces measurable output: response accuracy, time saved, task completion rates. Production rollout depends on integration complexity, data readiness, and compliance review. Most clients see operational impact within 8 to 12 weeks of starting. We define success metrics before we begin so there is no ambiguity about what \"working\" means."}},{"@type":"Question","name":"What does our team need to provide for an AI engagement?","acceptedAnswer":{"@type":"Answer","text":"It depends on where you are. If you have a defined use case, we need access to the relevant data or content, a measurable goal, and a point of contact from your business and IT teams. If you are still exploring where AI fits, that is where most engagements start. We run discovery sessions to map your workflows, identify high-impact opportunities,and scope a pilot with clear success criteria. Either way, we handle architecture, model selection, development, and deployment. Your team stays involved in validation and review, not in building or maintaining the system."}}]} ## Frequently asked questions ### We ran a pilot that worked. Why is production so different? Pilots run on clean data, limited users, and relaxed constraints. Production means integrating with legacy systems, handling edge cases, enforcing access controls, and monitoring for drift over time. Most AI projects stall here because the engineering and governance work was not scoped into the pilot. We structure pilots with production requirements built in from day one so the transition is planned, not improvised. ### How do you handle data sovereignty and compliance when building AI systems? Every AI engagement starts with understanding where your data lives, which regulations apply, and what your internal policies require. We architect AI systems so that data stays within your approved infrastructure, whether that is a specific cloud region, an on-premises environment, or a hybrid setup. Model training, inference, and storage are scoped to your jurisdictional and compliance requirements, including GDPR, HIPAA, and regional data residency laws. We do not default to a single hosting model. The architecture is shaped by your data governance needs, not ours. ### What does AI monitoring actually involve after deployment? After deployment, models can drift as data changes, user behaviour shifts, or source content is updated. Monitoring means tracking output quality, flagging accuracy drops, logging usage patterns, and running periodic evaluations against ground truth. It also includes governance reviews so teams can audit what the system produced and why. Without this, deployed AI degrades silently. ### Can AI integrate with our existing Drupal or CMS platform? Yes. Most of our AI work connects to existing content platforms, not replaces them. RAG systems pull from your CMS content. AI assistants surface answers from documents already in your system. Agents trigger actions in tools your teams use. If you are on Drupal, we have 17 years of platform expertise and lead the Drupal AI initiative, so the integration is tighter than what a standalone AI vendor would deliver. ### How long before we see measurable results from AI? A scoped AI pilot typically runs two weeks and produces measurable output: response accuracy, time saved, task completion rates. Production rollout depends on integration complexity, data readiness, and compliance review. Most clients see operational impact within 8 to 12 weeks of starting. We define success metrics before we begin so there is no ambiguity about what "working" means. ### What does our team need to provide for an AI engagement? It depends on where you are. If you have a defined use case, we need access to the relevant data or content, a measurable goal, and a point of contact from your business and IT teams. If you are still exploring where AI fits, that is where most engagements start. We run discovery sessions to map your workflows, identify high-impact opportunities,and scope a pilot with clear success criteria. Either way, we handle architecture, model selection, development, and deployment. Your team stays involved in validation and review, not in building or maintaining the system. ## From the Team ## Related Blogs [ ![Create your own AI agents for Drupal Canvas](/sites/default/files/clone-images/69b906dba1f65513cb7a95b3_Create_your_own_AI_agents_for_Drupal_Canvas_05f8c9db.webp) ](/insights/responsible-ai-testing-ensuring-ethical-fair-and-safe-ai-systems) AI ### [Responsible AI testing: ensuring ethical, fair, and safe AI systems](/insights/responsible-ai-testing-ensuring-ethical-fair-and-safe-ai-systems) [ ![HAAT Deep Dive Turning Human Intervention into a Scalable Primitive](/sites/default/files/clone-images/69b905fa51aaf6b30c1990de_HAAT_Deep_Dive_Turning_Human_Intervention_into_a_Scalable_Primitive_1_7d5733ea.webp) ](/insights/haat-deep-dive-turning-human-intervention-into-a-scalable-primitive) AI ### [HAAT deep dive: turning human intervention into a scalable primitive](/insights/haat-deep-dive-turning-human-intervention-into-a-scalable-primitive) [ ![I Built a Video RAG System Thats Cheaper Than Industry Standard Heres How](/sites/default/files/clone-images/69a593fe4a3c0f4ec86409f2_I_Built_a_Video_RAG_System_Thats_81_Cheaper_Than_Industry_Standard_-_Heres_How_063bd061.webp) ](/insights/building-a-video-rag-system-thats-81-cheaper-than-industry-standard-heres-how) AI ### [Building a video RAG system that's 81% cheaper than "Industry standard", here's how](/insights/building-a-video-rag-system-thats-81-cheaper-than-industry-standard-heres-how) [View All →](/insights) ### Bring your use case, we will build it Not sure where AI fits? We will help you figure that out You must have JavaScript enabled to use this form. First name Work email\* What best describes where you are? Exploring where AI fits Have a defined use case Ready to run a pilot Scaling AI to production Anything else you would like to share [Privacy policy](/privacy-policy) Leave this field blank ## Aeldris Aeldris lets you build AI assistants in minutes. From search and chat to document intelligence, you stay in control with orchestration, oversight, and real-time analytics. [ Try Aeldris ](https://aeldris.com/)